There exists also a large body of literature tackling localization and mapping changing environments, for example by filtering moving objects [1]}, considering residuals in matching [2]}, or by exploiting sequence information [3]}.
To achieve outdoor large-scale semantic SLAM, one can also combine 3D LiDAR sensors with RGB cameras. Yan et al.  [4]} associate 2D images and 3D points to improve the segmentation for detecting moving objects.
Wang and Kim [5]} use images and 3D point clouds from the KITTI dataset [6]} to jointly estimate road layout and segment urban scenes semantically by applying a relative location prior.
Jeong et al.  [7]}, [8]} also propose a multi-modal sensor-based semantic 3D mapping system to improve the segmentation results in terms of the intersection-over-union (IoU) metric, in large-scale environments as well as in environments with few features. Liang et al.  [9]} propose a novel 3D object detector that can exploit both LiDAR and camera data to perform accurate object localization. All of these approaches focus on combining 3D LiDAR and cameras to improve the object detection, semantic segmentation, or 3D reconstruction.
